{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection of filters for the model\n",
    "\n",
    "Select the parameters for Unet architecture, to suppress noise on the seismograms.\n",
    "\n",
    "* [Model description](#Model-description)\n",
    "* [The parameters studied](#The-parameters-studied)\n",
    "* [Parameters value area](#Parameters-value-area)\n",
    "* [Metrics](#Metrics)\n",
    "* [Dataset loading](#Dataset-loading)\n",
    "* [Dataset bypass](#Dataset-bypass)\n",
    "* [Creating a research object](#Creating-a-research-object)\n",
    "* [Results](#Results)\n",
    "* [Conclusion](#Conclusion)\n",
    "\n",
    "### Model description\n",
    "layout dictionary:\n",
    "* __c__ - 1d-convolutional block.\n",
    "* __a__ - activation function.\n",
    "* __d__ - dropout layer.\n",
    "* __t__ - 1d-transposed convolition.\n",
    "\n",
    "This is a Unet-like architecture with following blocks:\n",
    "* Encoder:\n",
    "    \n",
    "    Encoder could be decsribed by layout _ca ca_ where:\n",
    "    * c - block:\n",
    "        - kernel size = 7\n",
    "    * activation - _elu_\n",
    "* Decoder:\n",
    "    \n",
    "    Decoders' layout is _ca ca_ where:\n",
    "    * c - block:\n",
    "        - kernel size = 7\n",
    "    * activation - _elu_\n",
    "* Downsample:\n",
    "    \n",
    "    Downsamples' layout is _p d_ where:\n",
    "    * p - block:\n",
    "        - pool_size = 2\n",
    "        - pool_stride = 2\n",
    "    * d -block:\n",
    "        - dropout_rate = 0.05\n",
    "* Upsample:\n",
    "    \n",
    "    Upsamples' layout is _tad_ where:\n",
    "    * t - block:\n",
    "        - kernel_size = 7\n",
    "        - strides = 2\n",
    "    * d -block:\n",
    "        - dropout_rate = 0.05\n",
    "    * activation - _elu_\n",
    "    \n",
    "### The parameters studied\n",
    "A number of filters\n",
    "\n",
    "### Parameters value area\n",
    "* _Initial number of filters_ - $8, 16$\n",
    "\n",
    "* _Number of Unet blocks_ - $3, 5, 8.$\n",
    "                       \n",
    "### Metrics\n",
    "Pixel-based difference module between prediction and target.\n",
    "\n",
    "### Dataset loading\n",
    "Dataset is given by 2 SEGY files with a seismogram before and after ground-roll attenuation according to LIFT procedure. The seismorgam contains 176K traces combined in 51 field records. \n",
    "\n",
    "First, we index field records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors as mcolors\n",
    "import tensorflow as tf\n",
    "\n",
    "sys.path.append('../../..')\n",
    "\n",
    "from seismicpro.batchflow import Pipeline, B, V, C\n",
    "from seismicpro.batchflow.models.tf import UNet\n",
    "from seismicpro.batchflow.research import Research, Option\n",
    "from seismicpro.src import (SeismicDataset, FieldIndex, TraceIndex,\n",
    "                            show_research, print_results)\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_raw = '/data/NA/noise_dataset_1/DN02A_LIFT_AMPSCAL.sgy'\n",
    "path_lift = '/data/NA/noise_dataset_1/DN02B_SHOTS_LIFT1.sgy'\n",
    "\n",
    "index = (FieldIndex(name='raw', extra_headers=['offset'], path=path_raw)\n",
    "         .merge(FieldIndex(name='lift', path=path_lift)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset bypass:\n",
    "* Define model config\n",
    "* Define index and train / test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config = {\n",
    "    'initial_block/inputs': 'x',\n",
    "    'inputs': dict(x={'shape': (3000, 1)}, \n",
    "                   y={'name':'targets', 'shape': (3000, 1)}),\n",
    "    'body/filters': C('filters'),\n",
    "    'body/encoder': dict(layout='caca', kernel_size=7, activation=tf.nn.elu),\n",
    "    'body/downsample': dict(layout='pd', pool_size=2, pool_strides=2, dropout_rate=0.05),\n",
    "    'body/decoder': dict(layout='caca', kernel_size=7, activation=tf.nn.elu),\n",
    "    'body/upsample': dict(layout='tad', kernel_size=7, strides=2,\n",
    "                          dropout_rate=0.05, activation=tf.nn.elu),\n",
    "    'loss': 'l1',\n",
    "    'optimizer': 'Adam'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index = index.create_subset(index.indices[:5])\n",
    "train_set = SeismicDataset(TraceIndex(train_index))\n",
    "test_set = SeismicDataset(TraceIndex(index.create_subset(index.indices[20:21])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an instance of train and test pipelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_data(batch, **kwagrs):\n",
    "    return {'x': np.expand_dims(np.vstack(batch.raw), -1),\n",
    "            'y': np.expand_dims(np.vstack(batch.lift), -1)}\n",
    "\n",
    "B_SIZE = 32\n",
    "train_pipeline = (Pipeline()\n",
    "                  .load(components=('raw', 'lift'), fmt='segy', tslice=np.arange(3000))\n",
    "                  .init_variable('loss', init_on_each_run=list)\n",
    "                  .init_model('dynamic', UNet, 'unet', model_config)\n",
    "                  .train_model('unet', make_data=make_data,\n",
    "                               fetches='loss', save_to=V('loss', mode='w'))\n",
    "                  .run_later(B_SIZE, n_epochs=None, drop_last=True, shuffle=True)\n",
    "                 ) << train_set\n",
    "\n",
    "test_pipeline = (Pipeline()\n",
    "                    .import_model('unet', C('import_from'))\n",
    "                    .init_variable('res', init_on_each_run=list())\n",
    "                    .init_variable('raw', init_on_each_run=list())\n",
    "                    .init_variable('lift', init_on_each_run=list())\n",
    "                    .load(components=('raw', 'lift'), tslice=np.arange(3000), fmt='segy')\n",
    "                    .update_variable('raw', B('raw'), mode='a')\n",
    "                    .update_variable('lift', B('lift'), mode='a')\n",
    "                    .predict_model('unet', fetches='predictions', make_data=make_data,\n",
    "                                   save_to=V('res', mode='a'))\n",
    "                    .run_later(B_SIZE, n_epochs=1, drop_last=True, shuffle=True)\n",
    "                   ) << test_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define of auxiliary functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_l1(iteration, experiment, pipeline):\n",
    "    \"\"\" Calculate l1 norm.\"\"\"\n",
    "    _ = iteration\n",
    "    pipeline = experiment[pipeline].pipeline\n",
    "    res = np.squeeze(np.vstack(pipeline.v(\"res\")), axis=-1)\n",
    "    lift = np.vstack(np.concatenate(pipeline.v(\"lift\")))\n",
    "    return np.mean(np.abs(res - lift))\n",
    "\n",
    "def save_model(iteration, experiment, pipeline, model_name, path='./'):\n",
    "    \"\"\" Save model to a path.\"\"\"\n",
    "    path = os.path.join(path, experiment[pipeline].config.alias(as_string=True) + '_' + str(iteration))\n",
    "    pipeline = experiment[pipeline].pipeline\n",
    "    pipeline.save_model(model_name, path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a research object\n",
    "After pipeline creation let's define reserach instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "opts = Option('filters', [[8, 16, 32],\n",
    "                          [16, 32, 64],\n",
    "                          [8, 16, 32, 64, 128],\n",
    "                          [16, 32, 64, 128, 256],\n",
    "                          [8, 8, 16, 16, 32, 32, 64, 64, 128, 128],\n",
    "                          [16, 16, 32, 32, 64, 64, 128, 128, 256, 256]])\n",
    "research = (Research()\n",
    "            .add_pipeline(train_pipeline, variables='loss', name='train')\n",
    "            .add_pipeline(test_pipeline, name='test_ppl', execute=5,\n",
    "                          run=True, import_from='train')\n",
    "            .add_grid(opts)\n",
    "            .add_function(get_l1, returns='l1', name='test',\n",
    "                          execute=5, pipeline='test_ppl')\n",
    "            .add_function(save_model, execute=-1, pipeline='train',\n",
    "                          model_name='unet', path='saved_models/')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to train research instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Research reserach_filter is starting...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13500/13500 [39:46<00:00,  5.66it/s]\n"
     ]
    }
   ],
   "source": [
    "NUM_REPEAT = 10\n",
    "NUM_ITERS = 500\n",
    "research_name = 'reserach_filter'\n",
    "\n",
    "research.run(n_reps=NUM_REPEAT, n_iters=NUM_ITERS, name=research_name, workers=5,\n",
    "             gpu=[1, 2, 3, 6, 7], bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Loss functions vs a number of iterations for each parameters set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = research.load_results(use_alias=True)\n",
    "show_research(df, layout=['train/loss', 'test/l1'], average_repetitions=True, color=list(mcolors.TABLEAU_COLORS.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is difficult to understand from the charts which model shows the best quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_mean</th>\n",
       "      <th>test_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>filters_[16, 32, 64, 128, 256]</th>\n",
       "      <td>0.006453</td>\n",
       "      <td>0.000388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[16, 16, 32, 32, 64, 64, 128, 128, 256, 256]</th>\n",
       "      <td>0.006586</td>\n",
       "      <td>0.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64, 128]</th>\n",
       "      <td>0.006732</td>\n",
       "      <td>0.000675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 8, 16, 16, 32, 32, 64, 64, 128, 128]</th>\n",
       "      <td>0.007345</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[16, 32, 64]</th>\n",
       "      <td>0.012275</td>\n",
       "      <td>0.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32]</th>\n",
       "      <td>0.013314</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    test_mean  test_std\n",
       "filters_[16, 32, 64, 128, 256]                       0.006453  0.000388\n",
       "filters_[16, 16, 32, 32, 64, 64, 128, 128, 256,...   0.006586  0.000502\n",
       "filters_[8, 16, 32, 64, 128]                         0.006732  0.000675\n",
       "filters_[8, 8, 16, 16, 32, 32, 64, 64, 128, 128]     0.007345  0.000364\n",
       "filters_[16, 32, 64]                                 0.012275  0.000284\n",
       "filters_[8, 16, 32]                                  0.013314  0.000338"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = print_results(df, 'test/l1', True, sort_by='test_mean',ascending=True, n_last=100)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see, that the best quality shows model with following parameters:\n",
    "* _Initial number of filters_ - 16\n",
    "* _Number of Unet blocks_ - 5."
   ]
  }
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